fixing gallery

.envrc

@@ -0,0 +1,2 @@
+eval "$(conda shell.zsh hook)"
+conda activate vibesearch

app.py

@@ -1,10 +1,11 @@
+import os
+import pickle
+
 import gradio as gr
-from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
 import sentence_transformers
-from sentence_transformers import SentenceTransformer, util
-import pickle
 from PIL import Image
-import os
+from sentence_transformers import SentenceTransformer, util
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer
 
 # (Pdb) query_emb.shape
 # torch.Size([1, 512])
@@ -12,25 +13,24 @@ import os
 # (24996, 512)
 
 
-
 ## Define model
 model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
 processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
 tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
 
-#Open the precomputed embeddings
-emb_filename = 'lv-handbags.pkl'
+# Open the precomputed embeddings
+emb_filename = "lv-handbags.pkl"
 # emb_filename = 'unsplash-25k-photos-embeddings.pkl'
 
-with open(emb_filename, 'rb') as fIn:
-        img_names, img_emb = pickle.load(fIn)
-        #print(f'img_emb: {print(img_emb)}')
-        #print(f'img_names: {print(img_names)}')  
+with open(emb_filename, "rb") as fIn:
+    img_names, img_emb = pickle.load(fIn)
+    # print(f'img_emb: {print(img_emb)}')
+    # print(f'img_names: {print(img_names)}')
 
 
 def search_text(query, top_k=4):
-    """" Search an image based on the text query.
-    
+    """ " Search an image based on the text query.
+
     Args:
         query ([string]): [query you want search for]
         top_k (int, optional): [Amount of images o return]. Defaults to 1.
@@ -39,44 +39,49 @@ def search_text(query, top_k=4):
         [list]: [list of images that are related to the query.]
     """
     # First, we encode the query.
-    inputs = tokenizer([query],  padding=True, return_tensors="pt")
+    inputs = tokenizer([query], padding=True, return_tensors="pt")
     query_emb = model.get_text_features(**inputs)
 
     # import pdb; pdb.set_trace()
-    
+
     # Then, we use the util.semantic_search function, which computes the cosine-similarity
     # between the query embedding and all image embeddings.
     # It then returns the top_k highest ranked images, which we output
     hits = util.semantic_search(query_emb, img_emb, top_k=top_k)[0]
-    
-    image=[]
+
+    image = []
     for hit in hits:
-        #print(img_names[hit['corpus_id']])
+        # print(img_names[hit['corpus_id']])
         # object = Image.open(os.path.join("photos/", img_names[hit['corpus_id']]))
-        object = Image.open(os.path.join("lvphotos/", img_names[hit['corpus_id']]))
+        object = Image.open(os.path.join("lvphotos/", img_names[hit["corpus_id"]]))
         image.append(object)
-        #print(f'array length is: {len(image)}')
+        # print(f'array length is: {len(image)}')
 
     return image
-    
+
 
 iface = gr.Interface(
-    title = "Hushh Vibe Search Model on Louis Vuitton API",
-    description = "Quick demo of using text to perform vector search on an image collection",
-    article = "TBD",
-    fn=search_text, 
-    inputs=[gr.Textbox(lines=4,
-        label="Write what you are looking for in an image...",
-        placeholder="Text Here...")],
-    outputs=[gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[2], height="auto")]
-
-    ,examples=[
+    title="Hushh Vibe Search Model on Louis Vuitton API",
+    description="Quick demo of using text to perform vector search on an image collection",
+    article="TBD",
+    fn=search_text,
+    inputs=[
+        gr.Textbox(
+            lines=4,
+            label="Write what you are looking for in an image...",
+            placeholder="Text Here...",
+        )
+    ],
+    outputs=[
+        gr.Gallery(
+            label="Generated images", show_label=False, elem_id="gallery", columns=2
+        )
+    ],
+    examples=[
         [("Vacation Star")],
         [("Rock Star")],
         [("Barbie")],
         [("Small Purse")],
         [("Big Bag")],
-        ]
-    ).launch(debug=True)
+    ],
+).launch(debug=True)